用核磁共振实验验证量子核方法可高效处理量子数据
Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR
- 在3比特核磁共振平台上实现量子核学习，直接处理量子算符输入
- 实验验证了量子核方法在分类纠缠与非纠缠算符上的有效性
- 无需昂贵的量子态层析，适合在真实量子硬件上比较量子操作
量子核学习（QKL）通过将特征映射编码到量子系统固有的指数级希尔伯特空间中，有望实现高效的机器学习。我们利用液态核磁共振（NMR）平台，对一维回归和二维分类任务实施并基准测试了QKL。随后，通过将QKL扩展至参数化或非参数化算符输入，实现了对纠缠与非纠缠算符的分类。首先对双层星型系统数值计算核函数，再在3量子比特NMR寄存器上进行实验验证。该方法为在原生量子硬件上比较算符提供了实用途径，避免了昂贵的量子态层析。结果表明，相比其他经典方法，QKL在处理量子数据时具有明显优势，展现出捕捉量子空间内在结构的能力，并可通过利用算符空间中的对称性，将应用范围拓展至训练域之外。
原文摘要 · Abstract (English)
Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in quantum systems. Using the liquid-state nuclear magnetic resonance (NMR) platform, we implement and benchmark QKL for one-dimensional regression and two-dimensional classification tasks. We then classify entangling and non-entangling operators by extending QKL to handle parametrized or non-parameterized operator inputs. We first compute the kernel numerically for a double-layered star system and then experimentally validate it on a 3-qubit NMR register. QKL provides a practical route to compare operators on native quantum hardware without expensive tomography protocols. Our results confirm the superiority of QKL over other classical methods for processing quantum data, thereby highlighting its ability to capture the inherent structure of quantum space and to extend its domain of operation beyond the training domain by exploiting symmetries in the operator space.
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